Electrocardiograms are commonly used to generate data relating to the health and operation of a patient's heart. ECG data is useful to study heart rate variability, that is, variations in a patient's pulse over time. The study of heart rate variability (HRV) is useful as a noninvasive indicator of parasympathetic and sympathetic influences on the heart. HRV analysis has been used to study sudden infant death syndrome, autonomic neuropathy, brain stem status, brain death, the staging of diabetic neuropathy, hypertension, acute myocardial infarction, congestive heart failure, and sudden cardiac death.
The appeal of HRV analysis has increased with the development of Holter recorders. A Holter recorder is used to collect ECG data over a period of several days in ambulatory patients. Thus, ECG data can be collected in a setting that represents a patient's typical day. Holter recorders record large amounts of data that may be difficult for a clinician to analyze.
Several computer systems have been developed to perform statistical and spectral HRV analysis. These prior art systems, however, are not particularly useful to a clinician because of their lack of flexibility. The developers of these prior art systems have established certain methods for analyzing and displaying the HRV data. These methods, however, do not correspond to a preferred method of analyzing and displaying in many situations. For example, these prior art systems may not allow for the display of summary information over certain time periods (e.g., sleeping). Also, these systems do not permit the tailoring of the analysis to address a specific patient's heart rate condition.
It would be desirable to have an HRV system that would allow the clinician considerable freedom to control the statistical and spectral analysis in on-screen and printed format of reported data.